Identifying geospatial patterns from device data

ABSTRACT

Determining geospatial patterns from device data collected from a plurality of computing devices. The devices represent, for example, a plurality of sources providing the device data. The device data describes the computing devices and/or environments thereof. Some embodiments present the determined patterns to users for editing, update maps with the edited patterns, and distribute the maps to the users. The maps are stored to create a searchable map library.

This application is a continuation of commonly-owned, co-pending U.S.patent application Ser. No. 12/629,679, filed Dec. 2, 2009, the entiredisclosure of which is hereby incorporated by reference herein for allpurposes.

BACKGROUND

Existing electronic maps are created from various data sources such assatellite imagery, road vector data, terrain data, and the like. Themaps provide navigational assistance and serve purposes common to manyusers. With existing systems, users can create custom maps by addinglabels or text (e.g., My House, My Work, My School) to the maps.Further, some systems geocode photographs as the photographs arecaptured. These systems update the maps with the geocoded photographs,but the users have to provide the labels or other context for the mapsto have meaning. Such customization by the users, however, is tedious,error prone, and difficult to share with other users having similarinterests. Further, the updated maps are static.

SUMMARY

Embodiments of the disclosure enable the identification of geospatialpatterns based on device data. The device data describes a firstplurality of computing devices or environments thereof. From the devicedata, at least one geospatial pattern is identified. In someembodiments, map data is defined based on the identified geospatialpattern. The defined map data is transmitted to a second plurality ofcomputing devices (e.g., peers to the first plurality of computingdevices). The second plurality of computing devices each determinewhether to incorporate the defined map data into at least one map forpresentation to users of the second plurality of computing devices.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary block diagram illustrating a computing devicecollecting data from a plurality of mobile computing devices.

FIG. 2 is an exemplary block diagram illustrating the computing devicehaving a memory area storing the device data, geospatial patterns, andmaps.

FIG. 3 is an exemplary flow chart illustrating operation of thecomputing device inferring geospatial patterns and distributing map databased thereon.

FIG. 4 is an exemplary block diagram illustrating a group of peerdevices correlating device data and disseminating geospatial patternswithin the group.

Corresponding reference characters indicate corresponding partsthroughout the drawings.

DETAILED DESCRIPTION

Referring to the figures, embodiments of the disclosure enable, atleast, the creation of geospatial patterns 206 from device data 108.Some embodiments add the geospatial patterns 206 to one or more maps208. The geospatial patterns 206 include, for example, one or more ofthe following: a navigation path, a point of interest, one or more timeperiods, a weight value, a dwell time at a location (e.g., to indicatepopularity of the location), and a social event.

Aspects of the disclosure build socially driven, dynamic, temporal maps208 in real-time from a set of the patterns 206 (e.g., “grass roots”data), rather than from global sources of information. Embodiments ofthe disclosure facilitate open, community editing of the maps 208,display of semantic information on the maps 208, and creation oftemporal map archives. In some embodiments, the patterns 206 includetemporal geospatial patterns and the temporal maps 208 reflect thetemporal nature of the patterns 206. In other embodiments, the patterns206 are more permanent, and the maps 208 thus reflect the persistentnature of the patterns 206.

Referring again to FIG. 1, an exemplary block diagram illustrates acomputing device 104 collecting data from a plurality of mobilecomputing devices 102. In some embodiments, the collected datarepresents crowd-sourced data. The mobile computing devices 102, such asmobile computing device #1 through mobile computing device #N, includedevices such as mobile telephones, gaming consoles, and other handheldor vehicle-mounted devices. The mobile computing devices 102 eachinclude, or have access to, one or more sensors 110 that collect devicedata 108. Exemplary sensors 110 include network interfaces (e.g., wired,wireless, satellite, radio, BLUETOOTH brand interfaces), cameras,microphones, accelerometers, global positioning devices, thermometers,barometers, radio frequency identification (RFID) devices, radardetectors, and automotive vehicle information systems. The device data108 describes the mobile computing devices 102 or environments thereof,and includes the data collected by the sensors 110 as well as other datasuch as incoming and outgoing messages, device state data (e.g., idle oractive), and user-device interaction history. In some embodiments, thedevice data 108 describes one or more of the following: location,elevation, heading, speed, acceleration, direction, weather, messages(e.g., electronic mail messages, instant messages, blog messages,microblog messages, and the like), user interaction, image data (e.g.,photographs), voice data, text data, and files. As an example, thedevice data 108 includes social content data.

The mobile computing devices 102 communicate with the computing device104 via a network 106. Exemplary networks 106 include wired and wirelessnetworks. Exemplary wireless networks include one or more of wirelessfidelity (Wi-Fi) networks, BLUETOOTH brand networks, cellular networks,and satellite networks. In some embodiments, the computing device 104 isremote from the mobile computing devices 102. In other embodiments, thecomputing device 104 is local to the mobile computing devices 102, orone of the mobile computing devices 102.

In some embodiments, the computing device 104 operates to create anddistribute temporal maps 208 to the mobile computing devices 102. Forexample, the computing device 104 operates to collect the device data108 from the mobile computing devices 102, synthesize the device data108 to create the temporal maps 208, and distribute the created maps208, as described below with reference to FIG. 3. In furtherembodiments, one or more of the mobile computing devices 102 store thetemporal maps 208 and/or temporal geospatial patterns 112. The mobilecomputing devices 102 may also execute synthesis operations 114 toidentify the temporal geospatial patterns 112 and/or create the temporalmaps 208.

Referring next to FIG. 2, an exemplary block diagram illustrates thecomputing device 104 having a memory area 202 storing the device data108, geospatial patterns 206, and temporal maps 208. The device data 108is received by the computing device 104 from one or more other computingdevices (e.g., the mobile computing devices 102). In some embodiments,the computing device 104 creates the temporal maps 208 based on thegeospatial patterns 206. The computing device 104 includes at least thememory area 202 and a processor 204. The device data 108, geospatialpatterns 206, and/or temporal maps 208 may be stored locally as in theexample of FIG. 2, or stored remotely such as by a web service (e.g., acloud computing web service).

The memory area 202, or one or more computer-readable media, furtherstores computer-executable components for implementing aspects of thedisclosure. Exemplary components include an interface component 210, asynthesis component 212, a feedback component 214, a group component216, and a map component 218. These components are described below withreference to FIG. 3.

In general, the memory area 202 is associated with the computing device104. For example, in FIG. 2, the memory area 202 is within the computingdevice 104. However, the memory area 202 includes any memory areainternal to, external to, or accessible by computing device 104.Further, the memory area 202 or any of the data stored thereon may beassociated with any server or other computer, local or remote from thecomputing device 104 (e.g., accessible via a network).

The processor 204 includes any quantity of processing units, and isprogrammed to execute computer-executable instructions for implementingaspects of the disclosure. The instructions may be performed by theprocessor 204 or by multiple processors executing within the computingdevice 104, or performed by a processor external to the computing device104 (e.g., by a cloud service). In some embodiments, the processor 204is programmed to execute instructions such as those illustrated in thefigures (e.g., FIG. 3).

Referring next to FIG. 3, an exemplary flow chart illustrates operationof the computing device 104 inferring geospatial patterns 206 anddistributing map data based thereon. The computing device 104 accessesthe device data 108. In some embodiments, the device data 108 isassociated with one of the mobile computing devices 102 (or othercomputing devices), or associated with a plurality of the mobilecomputing devices 102 (or other computing devices). In an example, thecomputing device 104 accesses the device data 108 received during apre-defined time period (e.g., every hour, night, day, week, etc.).

In some embodiments, on-device synthesis is performed to identifypatterns from the device data 108 at 301. Each of the mobile computingdevices 102 collects and synthesizes the device data 108, and furtherfinds correlations based on other known data from a plurality of sources(e.g., calendar information, location, etc.). In an example, device data108 that includes the message “coffee with bridge team” is correlatedwith a current location to identify a nearby coffee shop. The correlatedmessage becomes “bridge game at coffee shop at 211 main street”, whichis presented at 304 to other computing devices.

In other embodiments, the on-device synthesis operations at 301 areperformed on device data 108 from a plurality of computing devices.

The computing device 104 identifies at least one geospatial pattern 206at 302 from the device data 108 and correlations (as available) from theon-device synthesis operations at 301. For example, the computing device104 analyzes the device data 108 for patterns 206 such as navigationalpatterns, user behavior, and other patterns. Additional examples areprovided below.

In some embodiments, the identified patterns 206 are presented to one ormore of the users at 304. Aspects of the invention contemplateoperations to validate the identified patterns 206 and scope thedistribution or presentation of the identified patterns 206 (or dataderived therefrom) to the users who are interested in and/or willbenefit from the patterns 206. For example, the identified patterns 206may be presented to the user whose device data 108 contributed toidentification of the patterns 206, or the identified patterns 206 maybe presented to a group of peer users or other selection of users. Theusers determine whether the identified patterns 206 are valid. Someembodiments of the disclosure further generate a confidence factor orother indicator of the correctness, accuracy, or reliability of theidentified patterns 206. The confidence factor is generated based on oneor more criteria and includes, for example, a weight value correspondto: a quantity of the devices contributing the device data 108 orotherwise associated with the identified patterns 206, similarity of thedevice data 108, a degree of correlation to other data including theuser data, and a location of the devices contributing the device data108. In some embodiments, the confidence factor is provided to the usersalong with the identified patterns 206. The users confirm or reject theidentified patterns 206 based on various factors including, for example,the provided confidence factor and/or the level of relevance or interestof the user in the identified patterns 206. For example, the level ofrelevance or interest may be determined from one or more of thefollowing: a defined user preference, user behavior history, and alocation of the user. Further, acceptance of the identified patterns 206may be explicit (e.g., the user explicitly accepts or rejects) and/orimplicit (e.g., a device of the user accepts or rejects based on, forexample, user preferences and/or past behavior).

If the presented pattern 206 is confirmed by the user(s) at 306, mapdata is created or updated at 308 using at least the identified patterns206. The map data represents metadata that is used to populate one ormore maps 208. The populated maps 208 each illustrate at least one ofthe geospatial patterns 206. In some embodiments, the map data may becreated using the identified patterns 206 along with user data or publicevent data to provide context for the identified patterns 206. In suchinstances, the user data is correlated with the identified patterns 206to provide the context. Exemplary user data includes calendar data,message data, task data, social networking data, location data, and apurchase history of the user. The user data is obtained from the memoryarea 202 (e.g., local to each computing device 104) or from anothermemory area remote from the computing device 104. An example includesidentifying a pattern in which multiple users traveling along a highwayperformed u-turns. Aspects of the disclosure may combine the identifiedpattern with knowledge of the proximity of the users to a stadium andwith knowledge of an upcoming event at the stadium to create map dataindicating that users heading to the event should take another route.

In some embodiments, the map data is not created or updated if thepresented pattern 206 is not confirmed. Instead, the presented pattern206 may be discarded, or otherwise identified as suspect.

Aspects of the disclosure may further store the map data and/or maps 208in a library, repository, or other structure for archival purposes. Insuch embodiments, the library represents a collection of temporal maps208 that may be searched, compared, or otherwise analyzed to identifyrelationships or trends among the geospatial patterns 206. In an examplein which the patterns 206 include navigation paths, the library isanalyzed to identify trends such as an increase in traffic incidentsduring particular times of the year (e.g., back to school, specialevents, etc.).

One or more of computing devices are selected at 310 to receive the mapdata. For example, the selected computing devices include a group ofcomputing devices that are peers to at least one of the computingdevices that contributed the device data 108 from which the patterns 206were identified. In an example in which the map data indicates a hazardat a location, the selected computing devices include devices near thelocation. In the example above in which aspects of the disclosuredetermine that users heading to an event at a stadium will encounter atraffic incident, aspects of the disclosure further identify users thatmay be heading to the game. Those users may have, for example, beenpre-registered for the game or be on a list of ticket purchasers for thegame. The devices selected at 310 then include the devices of thoseidentified users.

In another embodiment, the devices are selected at 310 based on aprevious selection of devices. For example, aspects of the disclosuredetermine whether any of the identified patterns 206 are related to apreviously identified pattern 206. Similarly, the map data or maps 208are compared to previously created map data or maps 208. The devicesselected at 310 then correspond to the devices selected to receive thepreviously identified pattern 206, map data, or maps 208. As an example,if a map of a traffic incident was previously delivered to a set ofdevices and a newly created map is determined to relate to the previousmap, the newly created map is sent to the same set of devices. In thismanner, users who were previously notified of a traffic incident may benotified of the clearing of the traffic incident.

At 312, the updated map data is transmitted or otherwise sent to thedevices selected at 310. In some embodiments, upon receipt of the mapdata, the selected devices each determine whether to incorporate theupdated map data into a map for presentation to the user of the selecteddevice. For example, each of the selected devices may compare the mapdata with a current location and/or direction of the device to determinewhether the map data is relevant to the user of the device. If the mapdata is relevant, the device incorporates the map data into the map. Inother embodiments, the selected devices automatically incorporate theupdated map data without additional screening, filtering, or otheranalysis.

Alternatively or in addition, one or more of the populated maps 208 withthe updated map data are sent at 310 to the selected devices. In suchembodiments, each of the selected devices may compare the map 208 with acurrent location and/or direction of the device to determine whether themap 208 is relevant to the user of the device. If the map 208 isrelevant, the device presents the map 208 to the user of the device.

In addition to sending the map data and/or the maps 208, recommendationsmay be sent to the user. In an example in which the map 208 illustratesa traffic incident, the recommendations include alternative routes. Someembodiments contemplate subscription functionality enabling the user tosubscribe to receive recommendations. In such embodiments, users that donot subscribe do not receive the recommendations. Exemplaryrecommendations include “80% of people who turned right on Main Streeteventually turned around and back-tracked to Elm Street,” and “50% ofpeople reported an event in progress up ahead and social networkmessages indicate a bike race on Mumble Street Bridge right now.” In thelatter example, the recommendation enables the users to confirm or altertheir navigation path.

In some embodiments, one or more computer-executable components, such asthe components illustrated in FIG. 2, execute on the computing device104 to perform the operations illustrated in FIG. 3. The interfacecomponent 210, when executed by the processor 204, causes the processor204 to receive the device data 108 from one or more of computingdevices. The synthesis component 212, when executed by the processor204, causes the processor 204 to identify at least one geospatialpattern 206 from the device data 108 received by the interface component210. The feedback component 214, when executed by the processor 204,causes the processor 204 to present the geospatial pattern 206identified by the synthesis component 212 to at least one user of thecomputing devices. The feedback component 214 identifies the user, forexample, based on a location of the user (e.g., relative to the temporalgeospatial pattern). The user confirms, edits, or rejects the geospatialpattern 206 and informs the interface component 210. Edits to thepattern 206 include corrections, additions, or deletions. In someembodiments, the user provides semantic information to add meaning tothe geospatial pattern 206. The edits, comments, acceptances, or otherfeedback provided by the user and subsequent users cumulatively improvesthe accuracy of the pattern 206. The group component 216, when executedby the processor 204, causes the processor 204 to propagate, based onthe response received by the interface component 210, the geospatialpattern 206 identified by the synthesis component 212 to one or more ofthe computing devices.

In some embodiments, the map component 218, when executed by theprocessor 204, causes the processor 204 to generate a temporal map 208based on the geospatial pattern 206 identified by the synthesiscomponent 212. For example, if the received device data 108 comprises aphotograph and the identified geospatial pattern 206 represents atraffic hazard, the generated temporal map 208 includes the photographand a caption identifying the traffic hazard. In further embodiments,the feedback component 214 may present the temporal map 208 to the userfor confirmation, editing, or rejection. In such embodiments, thetemporal map 208 represents an “open” map available for editing by theusers selected by the feedback component 214. Further, each of the usersmay limit the distribution of the edits (e.g., update the user's maponly, update maps in my social network, or update additional maps).Similarly, aspects of the disclosure may constrain how socially far anedit travels. For example, when two users make an update available in agroup, the update can be made available to the members of the group. Inanother example, when a threshold quantity of users of a populationmakes or accepts an edit to the map 208, the edit is applied to theentire population. In still another example, the edit may be forwardedto a commercial data feed (e.g., to correct an address).

In some embodiments, the device data 108 is collected from a firstplurality of computing devices, and the updated map data is sent to asecond plurality of computing devices. For example, the first pluralityof computing devices and the second plurality of computing devices arepeers. Some of the computing devices may be included within both thefirst plurality and the second plurality. In other embodiments, thefirst plurality and the second plurality include the same devices, suchas illustrated and next described with reference to FIG. 4.

In other embodiments, the operations illustrated in FIG. 3 are performedby a web service.

In the example of FIG. 1, the mobile computing devices 102 operate tocollect the device data 108 while the computing device 104 identifiesthe geospatial patterns 206 from the device data 108 aggregated from themobile computing devices 102. In other embodiments such as illustratedin FIG. 4, each of a plurality of peer devices collects the device data108 and identifies the patterns 206 from the collected device data 108.

Referring next to FIG. 4, an exemplary block diagram illustrates a group402 of peer devices 404 correlating device data 108 and disseminatinggeospatial patterns 206 within the group 402. The peer devices 404include, for example, peer device #1 through peer device #M. The group402 of peer devices 404 includes any quantity of peer devices 404, andthe quantity may differ per group. In some embodiments, the peer devices404 are considered peers because of some commonality among the peerdevices 404. For example, the peer devices 404 may be associated with aparticular user or organization, be within a particular geographiclocation, have expressed a particular interest, or the like. The group402 of peer devices 404 may also represent a social group. Exemplarysocial groups are defined by social graphs (e.g., degrees ofseparation), those who participate in an event, those who frequent thesame restaurant, those who like the same book, or other shared interestor experience.

The peer devices 404 in FIG. 4 each have an on-device correlation engine406. Each on-device correlation engine 406 operates to identify thegeospatial patterns 206 based on the device data 108 of the peer device404 on which the on-device correlation engine 406 operates. Exemplaryfunctionality of the on-device correlation engine 406 is described inFIG. 3. A peer device agent 408 on each of the peer devices 404 obtainsthe geospatial patterns 206 from the on-device correlation engine 406and communicates the geospatial patterns 206 to a group agent 410. Thegroup agent 410 coordinates peer-to-peer voting on the temporalgeospatial patterns 206 among the peer devices 404 and disseminates theresults. For example, the group agent 410 selects the geospatialpatterns 206 that have been confirmed by a threshold quantity of thepeer devices 404 (e.g., a majority). The group agent 410 distributes theselected geospatial patterns 206 to the peer devices 404.

The group agent 410 further acts as a liaison between the group 402 ofpeer devices 404 and a larger or otherwise different group 412 of peerdevices. In the example of FIG. 4, the group agent 410 provides theselected geospatial patterns 206 from the group 402 of peer devices tothe larger/different group 412 of peer devices. Peer-to-peer voting andresult dissemination occurs in the larger/different group 412 of peerdevices similar to such activities in the group 402 of peer devices.

Alternatively or in addition, the group agent 410 distributes temporalmap data, temporal maps 208, or any other data generated from thegeospatial patterns 206.

Further Examples

Various implementations of the disclosure are contemplated. In someembodiments, by correlating the user data with the geospatial patterns206, colloquial names may be identified. For example, if the geospatialpattern 206 indicates that a particular bridge is closed based onu-turns obtained from the device data 108 and the user data includesdirections listing a colloquial name for the bridge, aspects of thedisclosure associate the colloquial name with the bridge. Suchembodiments create a “geo-thesaurus” of geographic landmarks or otherlocations.

In another example, people visiting a building provide their device data108 to a system embodying aspects of the disclosure. The system infers(e.g., in combination with a calendar data feed from the users) apattern of people parking and then entering through a point in spacedeemed to be a door based on correlated map information about thebuilding. The system infers that the pattern corresponds to a parkinglot for the building with a certain degree of reliability, but there arepeople not going to the building who park in the lot and people parkingin the lot who do not go to the building). In such embodiments, thesystem may ask one or more of the users for express user feedback suchas “Is this where you park and enter the building?” The system asks theusers by expressly sending the question with thumbs up/thumbs down iconsor some other indicia soliciting a response. The confidence factor forthe pattern may accompany the question. In some embodiments, if thepattern is incorrect or incomplete, the user is asked to provide thecorrect or missing information.

As another example, if the user travels to Building X for a meeting withTeam Y yet ends up traveling to Building Z, aspects of the disclosuremay determine that Team Y has moved to Building Z.

In a navigational example, if a threshold quantity of people aredetecting as going to a bridge and turning around, aspects of thedisclosure attempt to infer a reason for the pattern (e.g., the bridgeis down, an event is underway, etc.). By asking for feedback from theuser, the common reason for users turning around may be inferred.

In an example in which the user captures a photograph of a building at alocation (e.g., the device data 108 includes the photograph), aspects ofthe disclosure update a map of the location with the photograph. Assuch, the photograph appears whenever the user displays maps of thelocation.

Exemplary Operating Environment

By way of example and not limitation, computer readable media comprisecomputer storage media and communication media. Computer storage mediastore information such as computer readable instructions, datastructures, program modules or other data. Communication media typicallyembody computer readable instructions, data structures, program modules,or other data in a modulated data signal such as a carrier wave or othertransport mechanism and include any information delivery media.Combinations of any of the above are also included within the scope ofcomputer readable media.

Although described in connection with an exemplary computing systemenvironment, embodiments of the invention are operational with numerousother general purpose or special purpose computing system environmentsor configurations. Examples of well known computing systems,environments, and/or configurations that may be suitable for use withaspects of the invention include, but are not limited to, mobilecomputing devices, personal computers, server computers, hand-held orlaptop devices, multiprocessor systems, gaming consoles,microprocessor-based systems, set top boxes, programmable consumerelectronics, mobile telephones, network PCs, minicomputers, mainframecomputers, distributed computing environments that include any of theabove systems or devices, and the like.

Embodiments of the invention may be described in the general context ofcomputer-executable instructions, such as program modules, executed byone or more computers or other devices. The computer-executableinstructions may be organized into one or more computer-executablecomponents or modules. Generally, program modules include, but are notlimited to, routines, programs, objects, components, and data structuresthat perform particular tasks 310 or implement particular abstract datatypes. Aspects of the invention may be implemented with any number andorganization of such components or modules. For example, aspects of theinvention are not limited to the specific computer-executableinstructions or the specific components or modules illustrated in thefigures and described herein. Other embodiments of the invention mayinclude different computer-executable instructions or components havingmore or less functionality than illustrated and described herein.

Aspects of the invention transform a general-purpose computer into aspecial-purpose computing device when configured to execute theinstructions described herein.

The embodiments illustrated and described herein as well as embodimentsnot specifically described herein but within the scope of aspects of theinvention constitute exemplary means for creating the temporal map 208based on geospatial pattern 206, and exemplary means for identifying thegeospatial pattern 206.

The order of execution or performance of the operations in embodimentsof the invention illustrated and described herein is not essential,unless otherwise specified. That is, the operations may be performed inany order, unless otherwise specified, and embodiments of the inventionmay include additional or fewer operations than those disclosed herein.For example, it is contemplated that executing or performing aparticular operation before, contemporaneously with, or after anotheroperation is within the scope of aspects of the invention.

When introducing elements of aspects of the invention or the embodimentsthereof, the articles “a,” “an,” “the,” and “said” are intended to meanthat there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.

Having described aspects of the invention in detail, it will be apparentthat modifications and variations are possible without departing fromthe scope of aspects of the invention as defined in the appended claims.As various changes could be made in the above constructions, products,and methods without departing from the scope of aspects of theinvention, it is intended that all matter contained in the abovedescription and shown in the accompanying drawings shall be interpretedas illustrative and not in a limiting sense.

What is claimed is:
 1. A system for creating temporal maps based ongeospatial patterns, said system comprising: a memory area for storingdevice data received from one or more of a plurality of mobile computingdevices, said device data describing said one or more of the pluralityof mobile computing devices or environments thereof; and a processorprogrammed to: infer at least one temporal geospatial pattern from thereceived device data; generate a temporal map based at least on theinferred temporal geospatial pattern; compare the temporal map to apreviously generated temporal map sent to the one or more of theplurality of mobile computing devices; select, based on the comparison,at least one of the one or more of the plurality of mobile computingdevices that were sent the previously generated temporal map; andprovide the generated temporal map to the selected at least one of theone or more of the plurality of mobile computing devices.
 2. The systemof claim 1, wherein the processor is further programmed to generate aconfidence factor associated with the inferred temporal geospatialpattern, the confidence factor being accompanied by a question forproviding missing information or correcting the inferred temporalgeospatial pattern.
 3. The system of claim 2, wherein the confidencefactor includes a weight value based on one or more of the following: aquantity of the mobile computing devices associated with the inferredtemporal geospatial pattern, the received device data, and a location ofthe one or more of the plurality of mobile computing devices.
 4. Thesystem of claim 1, wherein the device data describes one or more of thefollowing: location, elevation, heading, speed, acceleration, direction,weather, messages, user interaction, image data, voice data, text data,and files.
 5. The system of claim 1, wherein the temporal geospatialpattern includes one or more of the following: a navigation path, apoint of interest, one or more time periods, a weight value, a dwelltime at a location, and a social event.
 6. The system of claim 1,wherein a level of relevance associated with the inferred temporalgeospatial pattern is determined from one or more of the following: adefined user preference, user behavior history, and a location of auser.
 7. The system of claim 1, wherein the processor is furtherprogrammed to provide a context of the inferred temporal geospatialpattern based on public event data or user data of a user associatedwith one of the plurality of mobile computing devices.
 8. A methodcomprising: accessing, by a processor, device data received from a firstplurality of computing devices; identifying at least one geospatialpattern from the accessed device data; defining map data using theidentified at least one geospatial pattern; determining whether theidentified at least one geospatial pattern is related to a previouslyidentified pattern; selecting a second plurality of computing devicesfor receiving the defined map data based on the determination, thesecond plurality of computing devices corresponding to a third pluralityof computing devices previously selected to receive the previouslyidentified pattern.
 9. The method of claim 8, wherein accessing thedevice data comprises accessing device data received within apre-defined time period.
 10. The method of claim 8, further comprising:identifying users associated with the first plurality of computingdevices; and obtaining user data associated with the identified users,wherein identifying the at least one geospatial pattern comprisescorrelating the accessed device data with the obtained user data. 11.The method of claim 10, wherein obtaining the user data comprisesobtaining one or more of the following: calendar data, message data,task data, social networking data, location data, and a purchasehistory.
 12. The method of claim 8, wherein the defined map dataindicates a hazard at a location, and further comprising selecting thesecond plurality of computing devices based on the location.
 13. Themethod of claim 8, further comprising: identifying another geospatialpattern relating to said at least one geospatial pattern; and notifyingthe first plurality of computing devices of the identified anothergeospatial pattern.
 14. The method of claim 8, wherein defining the mapdata comprises defining recommendations based on the identified at leastone geospatial pattern.
 15. One or more computer storage media storingcomputer-executable components, said components comprising: an interfacecomponent that when executed by at least one processor causes the atleast one processor to receive device data from one or more of aplurality of computing devices, said device data describing said one ormore of the plurality of computing devices or environments thereof; asynthesis component that when executed by at least one processor causesthe at least one processor to identify at least one geospatial patternfrom the device data received by the interface component; and a groupcomponent that when executed by at least one processor causes the atleast one processor to propagate the at least one geospatial patternidentified by the synthesis component to a first one of the one or moreof the plurality of computing devices that is related to a second one ofthe one or more of the plurality of computing devices which contributedthe device data for identification of the at least one geospatialpattern.
 16. The computer storage media of claim 15, further comprisinga map component that when executed by at least one processor causes theat least one processor to generate a map based on the at least onegeospatial pattern identified by the synthesis component.
 17. Thecomputer storage media of claim 15, further comprising a feedbackcomponent that when executed by at least one processor causes the atleast one processor to identify at least one user for editing,confirming or rejecting the at least one geospatial pattern.
 18. Thecomputer storage media of claim 15, wherein the at least one geospatialpattern is selected for propagation based on responses received from athreshold quantity of the one or more of the plurality of computingdevices.
 19. The computer storage media of claim 15, wherein a reasonfor the identified at least one geospatial pattern is inferred based onthe device data received from a threshold quantity of the one or more ofthe plurality of computing devices.
 20. The method of claim 8, whereindetermining whether the identified at least one geospatial pattern isrelated to a previously identified pattern comprises determining whetherthe at least one geospatial pattern is associated with a same topic ofinterest as the previously identified pattern.